A Hybrid Protocol using Fuzzy Logic and Rough Set Theory for Target Coverage

2020 ◽  
Vol 13 ◽  
Author(s):  
Pooja Chaturvedi ◽  
A K Daniel
Keyword(s):  
Fuzzy Logic ◽  
Set Theory ◽  
Rough Set ◽  
Network Performance ◽  
Processing Time ◽  
Rough Set Theory ◽  
Set Cover ◽  
Target Coverage ◽  
Rule Set ◽  
Trust Calculation

Background: Target coverage is considered as a significant problem in the area of wireless sensor networks which usually aims at monitoring a given set of targets with the required confidence level so that network lifetime can be enhanced while considering the constraints of the resources. Objective: To maximize the lifetime of the sensor network and minimize the overhead involved in the scheduling approach, such that the pre specified set of targets is monitored for longer duration with the required confidence level. Methods: The paper uses a fuzzy logic system based on Mamdani inference in which the node status to remain in the active state is determined on the basis of coverage probability, trust values and node contribution. The rule set for determining the set cover is optimized by using the rough set theory which aims to determine the node validity for the trust calculation. Results: The results show that the proposed approach improved the network performance in terms of processing time, throughput and energy conservation by a factor of 50%, 74% and 74% respectively as compared to the existing approaches. Conclusion: The paper proposes a scheduling strategy of the nodes for target coverage as an enhancement to the energy efficient coverage protocol (EECP) on the basis of rough set theory. The rule set for determining the set cover is optimized by using the rough set theory so that the network performance is improved in terms of the processing time, throughput and energy consumption. Current and Future Developments: In this paper we have optimized the performance of EECP protocol by considering the concept of Rough Set Theory, which determined the validity of the nodes in the trust calculation. We have evaluated the performance through a number of simulations and the results show the comparative improvement in terms of the processing time, throughput and energy conservation. To utilize the unutilized nodes in the current round for the coverage enhancement is our future work. We also aim to study the effects of heterogeneity on the performance of the proposed protocol in future. Declaration: The authors declare that the research article is author’s original work and have not been sent/ published elsewhere.

scholarly journals Topology in the Alternative Set Theory and Rough Sets via Fuzzy Type Theory

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 432 ◽  
Author(s):  
Vilém Novák
Keyword(s):  
Fuzzy Logic ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Rough Sets ◽  
Type Theory ◽  
Rough Set Theory ◽  
Basic Concepts ◽  
Alternative Set

In this paper, we will visit Rough Set Theory and the Alternative Set Theory (AST) and elaborate a few selected concepts of them using the means of higher-order fuzzy logic (this is usually called Fuzzy Type Theory). We will show that the basic notions of rough set theory have already been included in AST. Using fuzzy type theory, we generalize basic concepts of rough set theory and the topological concepts of AST to become the concepts of the fuzzy set theory. We will give mostly syntactic proofs of the main properties and relations among all the considered concepts, thus showing that they are universally valid.

Analysis of Time-Series Data by Merging Decision Rules

2017 ◽  
Vol 21 (6) ◽  
pp. 1026-1033
Author(s):  
Yoshiyuki Matsumoto ◽  
Junzo Watada ◽  
◽  
Keyword(s):  
Time Series ◽  
Decision Rule ◽  
Set Theory ◽  
Rough Set ◽  
Time Series Data ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Series Data ◽  
Rule Set ◽  
Analysis Of Time Series

Rough set theory was proposed by Z. Pawlak in 1982. This theory enables the mining of knowledge granules as decision rules from a database, the web, and other sources. This decision rule set can then be used for data analysis. We can apply the decision rule set to reason, estimate, evaluate, or forecast an unknown object. In this paper, rough set theory is used for the analysis of time-series data. We propose a method to acquire rules from time-series data using regression. The trend of the regression line can be used as a condition attribute. We predict the future slope of the time-series data as decision attributes. We also use merging rules to further analyze the time series data.

Rough Set Theory and Fuzzy Logic Based Warehousing of Heterogeneous Clinical Databases

2017 ◽  
Vol 25 (03) ◽  
pp. 385-408 ◽  
Author(s):  
R. Saravana Kumar ◽  
G. Tholkappia Arasu
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Second Phase ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Clinical Databases ◽  
Efficient Data

Large amounts of data about the patients with their medical conditions are presented in the Medical databases. Analyzing all these databases is one of the difficult tasks in the medical environment. In order to warehouse all these databases and to analyze the patient’s condition, we need an efficient data mining technique. In this paper, an efficient data mining technique for warehousing clinical databases using Rough Set Theory (RST) and Fuzzy Logic is proposed. Our proposed methodology contains two phases – (i) Clustering and (ii) Classification. In the first phase, Rough Set Theory is used for clustering. Clustering is one of the data mining techniques for warehousing the heterogeneous data bases. Clustering technique is used to group data that have similar characteristics in the same cluster and also to group the data that have dissimilar characteristics with other clusters. After clustering the data, similar objects will be clustered in one cluster and the dissimilar objects will be clustered under another cluster. The RST can be reduced the complexity. Then in the second phase, these clusters are classified using Fuzzy Logic. Normally, Classification with Fuzzy Logic is generated more number of rules. Since the RST is utilized in our work, the classification using Fuzzy can be done with less amount of complexity. The proposed approach is evaluated using various clinical related databases from heart disease datasets – Cleveland, Switzerland and Hungarian. The performance analysis is based on Sensitivity, Specificity and Accuracy with different cluster numbers. The experimentation results show that our proposed methodology provides better accuracy result.

A New Criterion for Attribute Reduction Based on Variable Precision Rough Set Model

2011 ◽  
Vol 121-126 ◽  
pp. 1579-1584
Author(s):  
Hai Zhong Tan
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Variable Precision ◽  
Rule Set ◽  
Variable Precision Rough Set ◽  
New Criterion

The rule set which is acquired based on rough set theory can be classified into two categories: deterministic rules and probabilistic rules. Traditional attribute reduction definitions in variable precision rough set model cannot guarantee the rule properties, namely deterministic or probabilistic. In this paper, a new criterion for attribute reduction is put forward based on variable precision rough set model. The rule properties can be preserved during the process of attribute reduction. The relationships between the new reduct definition and available definitions, including Ziarko’s reduct definition and β lower distribution reduct definition are also discussed.

scholarly journals Efficient Rule Set Generation using Rough Set Theory for Classification of High Dimensional Data

2011 ◽  
pp. 129-136
Author(s):  
Prasanta Gogoi ◽  
Ranjan Das ◽  
B Borah ◽  
D K. Bhattacharyya
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Network Flow ◽  
Rough Set Theory ◽  
Real Life ◽  
Local Network ◽  
Upper Approximation ◽  
Inconsistent Data ◽  
Network Intrusion ◽  
Rule Set

In this paper, a rough set theory (RST) based approach is proposed to mine concise rules from inconsistent data. The approach deals with inconsistent data. At first, it computes the lower and upper approximation for each concept, then adopts a learning from an algorithm to build concise classification rules for each concept satisfying the given classification accuracy. Lower and upper approximation estimation is designed for the implementation, which substantially reduce the computational complexity of the algorithm. UCI ML Repository datasets are used to test and validate the proposed approach. We have also used our approach on network intrusion dataset captured using our local network from network flow. The results show that our approach produces effective and minimal rules and provide satisfactory accuracy over several real life datasets.

Rule Extraction and Rule Evaluation Based on Granular Computing

2010 ◽  
pp. 196-242
Author(s):  
Jiye Liang ◽  
Yuhua Qian ◽  
Deyu Li
Keyword(s):  
Decision Rule ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Rule Extraction ◽  
Decision Table ◽  
Rule Set ◽  
Decision Tables ◽  
Rule Evaluation

In rough set theory, rule extraction and rule evaluation are two important issues. In this chapter, the concepts of positive approximation and converse approximation are first introduced, which can be seen as dynamic approximations of target concepts based on a granulation order. Then, two algorithms for rule extraction called MABPA and REBCA are designed and applied to hierarchically generate decision rules from a decision table. Furthermore, to evaluate the whole performance of a decision rule set, three kinds of measures are proposed for evaluating the certainty, consistency and support of a decision-rule set extracted from a decision table, respectively. The experimental analyses on several decision tables show that these three new measures are adequate for evaluating the decision performance of a decision-rule set extracted from a decision table in rough set theory. The measures may be helpful for determining which rule extraction technique should be chosen in a practical decision problem.

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